Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral?derivative controller in automatic voltage regulator system

Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.     

Single network adaptive critic design for power system stabilisers

The recently developed single network adaptive critic (SNAC) design has been used in this study to design a power system stabiliser (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. PSS design is formulated as a discrete non-linear quadratic regulator problem. SNAC is then used to solve the resulting discrete-time optimal control problem. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a single machine infinite bus test system for various system and loading conditions. The proposed stabiliser, which is relatively easier to synthesise, consistently outperformed stabilisers based on conventional lead-lag and linear quadratic regulator designs.     

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.     

Robust low-order controller design for multi-modal power oscillation damping using flexible AC transmission systems devices

Damping of multi-modal oscillation through supplementary control of a single flexible AC transmission system (FACTS) device is illustrated here. This often requires multiple feedback signals in a centralised multi-input single-output framework for which extension of the classical control design approaches is not straight forward. Past contributions have either focused on decentralised design of low-order PSS in an SISO or MIMO framework; or alternatively, on robust control design techniques which of course, result in higher order controllers. An attempt to design a fixed (low)-order controller, which is robust and is able to damp multiple swing modes with a single FACTS device is presented. The control design problem is formulated as a multi-objective parameter optimisation and solved using a standard evolutionary optimisation technique. Possible post-contingency operating conditions are considered explicitly during the design phase itself to reduce the conservativeness. The present exercise is a step forward towards use of wide area measurement systems for closed-loop supplementary control (around the primary voltage and/or power flow control loop) of the FACTS devices to improve the transfer capacity of the existing corridors.     

Coordinated parameter design of STATCOM stabiliser and PSS using MSSA algorithm

A nonlinear programming model for simultaneously coordinated parameters design of power system stabiliser (PSS) and thyristor-based static synchronous compensator (STATCOM) stabiliser is presented. A modified simplex-simulated annealing (MSSA) algorithm is developed for solving the programming model. The MSSA can shift all eigenvalues of the system into specified regions on the s-plane for the preconfigured multiple operational points. The MSSA algorithm combines the merits of conventional simplex and simulated annealing methods together, such as global optimal solution, robustness to initial parameter settings and acceptable convergence speed and so on and also improves the ability of solving constrained optimisation problems. Numerical results including eigenvalue analysis and the nonlinear simulation on the 10-generator New England test power system are presented to indicate the effectiveness and potential engineering applications of the MSSA algorithm.     

Inverse problems in the presence of inclusions and unilateral constraints: a boundary element approach

The authors present a boundary element method (BEM) numerical procedure for the solution of 2D non-destructive identification problems in the presence of unilateral boundary conditions. Firstly, the position of a deformable inclusion in frictionless unilateral contact with the matrix is identified on the basis of measurements surveyed at some sensor points on the external boundary where given static loads are applied (identification problem). Then the procedure used is extended to the identification of the position which a rigid/deformable inclusion must occupy within the matrix in order to maximise the structural stiffness of the matrix-inclusion system under prescribed external loads (optimisation problem). Matrix and deformable inclusion are both considered linear elastic. A minimisation problem is stated with design variables representing the size and the shape of the inclusion. The cost function is an error function that evaluates the difference between computed and observed displacements at the sensor points in the identification problem and the strain energy accumulated by the matrix-inclusion system in the optimisation problem. The minimisation is performed by using a first-order nonlinear optimisation technique in which the cost function gradient is computed by implicit differentiation. Some simple but meaningful examples are presented and discussed in order to show the applicability of the proposed technique. Received 30 August 2000     

Optimisation of binary integrators for decentralised detection

Binary integrators are an important part of the receiver in many operating radar systems. The optimisation of a binary integrator is not a simple task, because it requires the solution of a (k x n)-dimensional nonlinear optimisation problem, where n is the number of integrated bits (or the number of sensors in a distributed radar or sensor network) and k is the number of the design parameters of the single-pulse detector. An algorithm that converts the multi-dimensional optimisation problem into a one-dimensional problem, so reducing considerably the computational complexity, is developed. This reduction in computational complexity makes the real-time optimisation possible and practical, so it is very helpful for mobile sites in which the optimisation should be performed continually. The proposed algorithm can be applied when either the 'AND' or the 'OR' integration rule is adopted. The results are illustrated by means of two study cases. In the first case, the binary integrator of a constant false alarm rate radar detector is optimised; in the second one a decentralised detection system composed by n similar sensors is considered and the decision rules are jointly optimised according to the Neyman-Pearson criterion.     

Radial basis probabilistic neural network for differential protection of power transformer

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB.     

Tool path generation via the multi-criteria optimisation for flat-end milling of sculptured surfaces

A method of generating optimal tool paths for sculptured surface machining with flat-end cutters is presented in this paper. The inclination and tilt angles, as well as the feed directions of the cutter at each cutter contact point on a machining path are optimised as a whole so that the machining width of the tool path can be as large as possible, and concerns such as smooth cutter motion, gouging avoidance, scallop height and machining widths overlap are also considered when calculating a path. A multi-criteria tool path optimisation model is introduced, and it is converted into a single objective optimisation with the weighted sum method. The Differential Evolution (DE) algorithm is suitable for solving this highly non-linear problem. However, the searching process of the DE algorithm may be trapped in local minima due to large number of design variables. Therefore, an algorithm combining the DE algorithm and the sequence linear programming algorithm is developed to solve this optimisation model. The proposed method is applied to two freeform surfaces to illustrate its effectiveness.     

Smart box-enabled product–service system for cloud logistics

Modern logistics takes significant progress and rapid developments with the prosperity of E-commerce, particularly in China. Typical challenges that logistics industry is facing now are composed by a lack of sharing, standard, cost-effective and environmental package and efficient optimisation method for logistics tasks distribution. As a result, it is difficult to implement green, sustainable logistics services. Three important technologies, Physical Internet (PI), product–service system (PSS) and cloud computing (CC), are adopted and developed to address the above issues. PI is extended to design a world-standard green recyclable smart box that is used to encapsulate goods. Smart box-enabled PSS is constructed to provide an innovative sustainable green logistics service, and high-quality packaging, as well as reduce logistics cost and environmental pollution. A real-time information-driven logistics tasks optimisation method is constructed by designing a cloud logistics platform based on CC. On this platform, a hierarchical tree-structure network for customer orders (COs) is built up to achieve the order-box matching of function. Then, a distance clustering analysis algorithm is presented to group and form the optimal clustering results for all COs, and a real-time information-driven optimisation method for logistics orders is proposed to minimise the unused volume of containers. Finally, a case study is simulated to demonstrate the efficiency and feasibility of proposed cloud logistics optimisation method.     

Transient stability constrained optimal power flow using particle swarm optimisation

A novel approach based on the particle swarm optimisation (PSO) technique is proposed for the transient-stability constrained optimal power flow (TSCOPF) problem. Optimal power flow (OPF) with transient-stability constraints considered is formulated as an extended OPF with additional rotor angle inequality constraints. For this nonlinear optimisation problem, the objective function is defined as minimising the total fuel cost of the system. The proposed PSO-based approach is demonstrated and compared with conventional OPF as well as a genetic algorithm based counterpart on the IEEE 30-bus system. Furthermore, the effectiveness of the PSO-based TSCOPF in handling multiple contingencies is illustrated using the New England 39-bus system. Test results show that the proposed approach is capable of obtaining higher quality solutions efficiently in the TSCOPF problem     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

Application of a z-transform signal model and median filtering for power system frequency and phasor measurements

A z-transform signal model that when combined with a nonlinear post-filtering scheme is able to estimate the operating frequency and voltage/current phasors in a power system is developed. The signal model parameters are identified by an optimisation method in which the error between the model output and the actual signal that represents a voltage or current in the power system is minimised. The form and structure of the signal model do not require iterations in the optimisation process for parameter identification. The system operating frequency is directly evaluated from the model parameters. Noise effects and possible mismatches between the model and the actual signal are countered very effectively by applying a median post-filtering process to the time series representing the frequency estimates derived from the model. Extensive simulation studies and comparisons with existing frequency estimation techniques confirm the high performance of the proposed method in terms of accuracy and time delay. The accurate estimation of the operating frequency, achieved by the proposed method, allows voltage and/or current phasors in power systems to be measured or determined more precisely.     

Load-frequency control: a GA-based multi-agent reinforcement learning

The load-frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional-integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias (-) estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on - estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.     

Multi-criteria optimisation-based dynamic scheduling for controlling FMS

This study deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Most studies that address this issue use some sort of adaptive scheduling that enables the FMS to cope with the randomness and variability efficiently. The methods presented in the literature are usually based on heuristics and use simple dispatching rules. They do not consider changing the decision criteria dynamically as the system conditions change. In contrast to previous studies, the present study focuses on developing a control mechanism for dynamic scheduling that is based on incremental optimisation. This means that each time a scheduling decision is made, the local optimisation problem is solved such that the next jobs to be processed on machines are selected. The objective function (dominant decision criterion) for this optimisation problem is selected dynamically based on production order requirements, actual shop-floor status and system priorities. The proposed multi-criteria optimisation-based dynamic scheduling methodology was evaluated and compared with some known scheduling rules/policies. The results obtained demonstrate the superiority of the suggested methodology as well as its capability to cope with a multi-criteria environment.     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

Acceptance sampling is a useful tool for determining whether submitted lots should be accepted or rejected. With the current increase in outsourcing production processes and the high-quality levels required, it is very desirable to have an efficient and economic sampling scheme. This paper develops a variables repetitive group sampling (RGS) plan that accounts for the process yield (meeting the manufacturing specifications) and the quality loss (variation from the target). The plan parameters are determined by solving a nonlinear optimisation problem. This implies that the plan parameters minimise the average sample number required for inspection and fulfil the classical two-point conditions on the operating characteristic (OC) curve. Besides, this paper investigates the efficiency of the proposed plan and compares it with the existing variables single sampling plan. Tables of the plan parameters for the proposed variables RGS plan are provided and an application example is presented for illustration.     

Optimum part deposition orientation for multiple objectives in SL and SLS prototyping

In the present work an attempt has been made to achieve minimum average part surface roughness (best overall surface quality), minimum build time and support structure for stereolithography (SL) and selective laser sintering (SLS) processed parts by determining optimum part deposition orientation. A conventional optimisation algorithm based on a trust region method (available with MATLAB-7 optimisation tool box) has been used to solve the multi-objective optimisation problem. It is observed that the problem is highly multi-modal in nature and a suitable initial guess, which is used as an input to execute the optimisation module, is important to achieve a global optimum. A simple methodology has been proposed to find out the initial guess so that global minimum is obtained. Finally the surface roughness simulation is carried out with optimum part deposition orientation to have an idea of surface roughness variation over the entire part's surface before depositing the part. Case studies are presented to demonstrate the capabilities of the developed system. The major achievements of this work are consideration of multiple objectives for the two rapid prototyping processes, successful use of conventional optimisation algorithm available with MATLAB to handle multiple objectives and development of graphical user interface-based system.     

Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B&B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B&B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.     

Availability optimisation for stochastic degrading systems under imperfect preventive maintenance

This paper deals with imperfect preventive maintenance (PM) optimisation problem. The system to be maintained is typically a production system assumed to be continuously monitored and subject to stochastic degradation. To assess such degradation, the proposed maintenance model takes into account both corrective maintenance (CM) and PM. The system undergoes PM whenever its reliability reaches an appropriate value, while CM is performed at system failure. After a given number of maintenance actions, the system is preventively replaced by a new one. Both CM as well as PM are considered imperfect, i.e. they bring the system to an operating state which lies between two extreme states, namely the as bad as old state and as good as new state. The imperfect effect of CM and PM is modelled on the basis of the hybrid hazard rate model. The objective of the proposed PM optimisation model consists on finding the optimal reliability threshold together with the optimal number of PM actions to maximise the average availability of the system. A mathematical model is then proposed. To solve this problem an algorithm is provided. A numerical example is presented to illustrate the proposed maintenance optimisation model.